from sklearn_benchmarks.reporting.hp_match import HpMatchReporting
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
reporting = HpMatchReporting(against_lib="sklearnex", config="config.yml", log_scale=True)
reporting.make_report()
We assume here there is a perfect match between the hyperparameters of both librairies. For a given set of parameters and a given dataset, we compute the speedup
time scikit-learn / time sklearnex. For instance, a speedup of 2 means that sklearnex is twice as fast as scikit-learn for a given set of parameters and a given dataset.
KNeighborsClassifier_brute_force¶scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)
All estimators share the following parameters: algorithm=brute.
| estimator | function | n_samples_train | n_samples | n_features | parameters_digest | dataset_digest | mean_duration_sklearn | std_duration_sklearn | n_iter | iteration_throughput | latency | algorithm | n_jobs | n_neighbors | accuracy_score_sklearn | mean_duration_sklearnex | std_duration_sklearnex | accuracy_score_sklearnex | speedup | std_speedup | diff_accuracy_scores | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | e70d8e7886766d41f30889506baa1e67 | 2cf49829391aa7891e877f2ed070adf0 | 1.944462 | 0.149903 | NaN | 0.000411 | 0.001944 | brute | -1 | 1 | 0.663 | 0.175844 | 0.005268 | 0.687 | 11.057867 | 11.062828 | 0.024 |
| 4 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | 22d5eac4a4146149b35eae6914921514 | 2cf49829391aa7891e877f2ed070adf0 | 2.760593 | 0.035551 | NaN | 0.000290 | 0.002761 | brute | -1 | 5 | 0.757 | 0.174119 | 0.000250 | 0.742 | 15.854663 | 15.854679 | 0.015 |
| 7 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | 16f94fc93942ff7ece860c9b0f64645f | 2cf49829391aa7891e877f2ed070adf0 | 2.031129 | 0.008811 | NaN | 0.000394 | 0.002031 | brute | 1 | 100 | 0.882 | 0.209325 | 0.000582 | 0.875 | 9.703241 | 9.703279 | 0.007 |
| 8 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 100 | 16f94fc93942ff7ece860c9b0f64645f | 2cf49829391aa7891e877f2ed070adf0 | 0.020564 | 0.000214 | NaN | 0.000039 | 0.020564 | brute | 1 | 100 | 1.000 | 0.008704 | 0.000290 | 0.000 | 2.362613 | 2.363920 | 1.000 |
| 10 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | 2d5f2b2c02d77766434d9e5033b4a76c | 2cf49829391aa7891e877f2ed070adf0 | 2.763092 | 0.032432 | NaN | 0.000290 | 0.002763 | brute | -1 | 100 | 0.882 | 0.209494 | 0.002708 | 0.875 | 13.189357 | 13.190459 | 0.007 |
| 11 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 100 | 2d5f2b2c02d77766434d9e5033b4a76c | 2cf49829391aa7891e877f2ed070adf0 | 0.024565 | 0.002537 | NaN | 0.000033 | 0.024565 | brute | -1 | 100 | 1.000 | 0.008928 | 0.000887 | 0.000 | 2.751440 | 2.764992 | 1.000 |
| 13 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | efac583623711d395ddae7a1e881ff68 | 2cf49829391aa7891e877f2ed070adf0 | 2.010532 | 0.001644 | NaN | 0.000398 | 0.002011 | brute | 1 | 5 | 0.757 | 0.174371 | 0.000837 | 0.742 | 11.530172 | 11.530304 | 0.015 |
| 16 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | b79d63bb7670c492de5c3befac58fe29 | 2cf49829391aa7891e877f2ed070adf0 | 1.161442 | 0.009139 | NaN | 0.000689 | 0.001161 | brute | 1 | 1 | 0.663 | 0.174014 | 0.002288 | 0.687 | 6.674435 | 6.675012 | 0.024 |
| 19 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | e70d8e7886766d41f30889506baa1e67 | 88843f54689e3271092f70126e1de585 | 1.732155 | 0.015052 | NaN | 0.000009 | 0.001732 | brute | -1 | 1 | 0.896 | 0.025767 | 0.000198 | 0.967 | 67.222523 | 67.224508 | 0.071 |
| 22 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | 22d5eac4a4146149b35eae6914921514 | 88843f54689e3271092f70126e1de585 | 2.637346 | 0.019484 | NaN | 0.000006 | 0.002637 | brute | -1 | 5 | 0.922 | 0.026808 | 0.000165 | 0.974 | 98.378771 | 98.380631 | 0.052 |
| 25 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | 16f94fc93942ff7ece860c9b0f64645f | 88843f54689e3271092f70126e1de585 | 1.938979 | 0.001989 | NaN | 0.000008 | 0.001939 | brute | 1 | 100 | 0.929 | 0.060327 | 0.002021 | 0.975 | 32.140915 | 32.158949 | 0.046 |
| 28 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | 2d5f2b2c02d77766434d9e5033b4a76c | 88843f54689e3271092f70126e1de585 | 2.668220 | 0.018521 | NaN | 0.000006 | 0.002668 | brute | -1 | 100 | 0.929 | 0.062120 | 0.005806 | 0.975 | 42.952673 | 43.139885 | 0.046 |
| 31 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | efac583623711d395ddae7a1e881ff68 | 88843f54689e3271092f70126e1de585 | 1.928455 | 0.003849 | NaN | 0.000008 | 0.001928 | brute | 1 | 5 | 0.922 | 0.026792 | 0.000103 | 0.974 | 71.980002 | 71.980537 | 0.052 |
| 34 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | b79d63bb7670c492de5c3befac58fe29 | 88843f54689e3271092f70126e1de585 | 1.054937 | 0.001912 | NaN | 0.000015 | 0.001055 | brute | 1 | 1 | 0.896 | 0.025884 | 0.000248 | 0.967 | 40.756750 | 40.758627 | 0.071 |
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | iteration_throughput | latency | n_jobs | n_neighbors | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 100 | 0.011 | 0.000 | 7.052 | 0.0 | -1 | 1 | 0.048 | 0.005 | 0.238 | 0.239 | See | See |
| 3 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 100 | 0.011 | 0.000 | 7.057 | 0.0 | -1 | 5 | 0.045 | 0.000 | 0.250 | 0.250 | See | See |
| 6 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 100 | 0.011 | 0.000 | 7.007 | 0.0 | 1 | 100 | 0.046 | 0.000 | 0.247 | 0.247 | See | See |
| 9 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 100 | 0.011 | 0.000 | 7.014 | 0.0 | -1 | 100 | 0.046 | 0.000 | 0.249 | 0.249 | See | See |
| 12 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 100 | 0.013 | 0.004 | 6.127 | 0.0 | 1 | 5 | 0.046 | 0.000 | 0.283 | 0.283 | See | See |
| 15 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 100 | 0.011 | 0.000 | 7.028 | 0.0 | 1 | 1 | 0.046 | 0.000 | 0.248 | 0.248 | See | See |
| 18 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 2 | 0.004 | 0.000 | 0.377 | 0.0 | -1 | 1 | 0.008 | 0.000 | 0.514 | 0.514 | See | See |
| 21 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 2 | 0.004 | 0.000 | 0.379 | 0.0 | -1 | 5 | 0.008 | 0.000 | 0.513 | 0.513 | See | See |
| 24 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 2 | 0.004 | 0.000 | 0.381 | 0.0 | 1 | 100 | 0.008 | 0.000 | 0.507 | 0.507 | See | See |
| 27 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 2 | 0.004 | 0.000 | 0.381 | 0.0 | -1 | 100 | 0.008 | 0.000 | 0.510 | 0.510 | See | See |
| 30 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 2 | 0.004 | 0.000 | 0.375 | 0.0 | 1 | 5 | 0.008 | 0.000 | 0.517 | 0.517 | See | See |
| 33 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 2 | 0.004 | 0.000 | 0.380 | 0.0 | 1 | 1 | 0.008 | 0.000 | 0.510 | 0.510 | See | See |
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | iteration_throughput | latency | n_jobs | n_neighbors | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | 1.944 | 0.150 | 0.000 | 0.002 | -1 | 1 | 0.176 | 0.005 | 11.058 | 11.063 | See | See |
| 2 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 100 | 0.024 | 0.003 | 0.000 | 0.024 | -1 | 1 | 0.009 | 0.000 | 2.818 | 2.818 | See | See |
| 4 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | 2.761 | 0.036 | 0.000 | 0.003 | -1 | 5 | 0.174 | 0.000 | 15.855 | 15.855 | See | See |
| 5 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 100 | 0.023 | 0.002 | 0.000 | 0.023 | -1 | 5 | 0.009 | 0.000 | 2.720 | 2.721 | See | See |
| 7 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | 2.031 | 0.009 | 0.000 | 0.002 | 1 | 100 | 0.209 | 0.001 | 9.703 | 9.703 | See | See |
| 8 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 100 | 0.021 | 0.000 | 0.000 | 0.021 | 1 | 100 | 0.009 | 0.000 | 2.363 | 2.364 | See | See |
| 10 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | 2.763 | 0.032 | 0.000 | 0.003 | -1 | 100 | 0.209 | 0.003 | 13.189 | 13.190 | See | See |
| 11 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 100 | 0.025 | 0.003 | 0.000 | 0.025 | -1 | 100 | 0.009 | 0.001 | 2.751 | 2.765 | See | See |
| 13 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | 2.011 | 0.002 | 0.000 | 0.002 | 1 | 5 | 0.174 | 0.001 | 11.530 | 11.530 | See | See |
| 14 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 100 | 0.021 | 0.000 | 0.000 | 0.021 | 1 | 5 | 0.009 | 0.000 | 2.407 | 2.408 | See | See |
| 16 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | 1.161 | 0.009 | 0.001 | 0.001 | 1 | 1 | 0.174 | 0.002 | 6.674 | 6.675 | See | See |
| 17 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 100 | 0.020 | 0.000 | 0.000 | 0.020 | 1 | 1 | 0.009 | 0.000 | 2.322 | 2.322 | See | See |
| 19 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | 1.732 | 0.015 | 0.000 | 0.002 | -1 | 1 | 0.026 | 0.000 | 67.223 | 67.225 | See | See |
| 20 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 2 | 0.004 | 0.002 | 0.000 | 0.004 | -1 | 1 | 0.001 | 0.000 | 6.722 | 6.739 | See | See |
| 22 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | 2.637 | 0.019 | 0.000 | 0.003 | -1 | 5 | 0.027 | 0.000 | 98.379 | 98.381 | See | See |
| 23 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 2 | 0.009 | 0.002 | 0.000 | 0.009 | -1 | 5 | 0.001 | 0.000 | 14.430 | 14.465 | See | See |
| 25 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | 1.939 | 0.002 | 0.000 | 0.002 | 1 | 100 | 0.060 | 0.002 | 32.141 | 32.159 | See | See |
| 26 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 2 | 0.003 | 0.000 | 0.000 | 0.003 | 1 | 100 | 0.001 | 0.000 | 3.939 | 3.953 | See | See |
| 28 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | 2.668 | 0.019 | 0.000 | 0.003 | -1 | 100 | 0.062 | 0.006 | 42.953 | 43.140 | See | See |
| 29 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 2 | 0.005 | 0.002 | 0.000 | 0.005 | -1 | 100 | 0.001 | 0.000 | 7.286 | 7.332 | See | See |
| 31 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | 1.928 | 0.004 | 0.000 | 0.002 | 1 | 5 | 0.027 | 0.000 | 71.980 | 71.981 | See | See |
| 32 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 2 | 0.003 | 0.000 | 0.000 | 0.003 | 1 | 5 | 0.001 | 0.000 | 4.310 | 4.324 | See | See |
| 34 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | 1.055 | 0.002 | 0.000 | 0.001 | 1 | 1 | 0.026 | 0.000 | 40.757 | 40.759 | See | See |
| 35 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 2 | 0.002 | 0.000 | 0.000 | 0.002 | 1 | 1 | 0.001 | 0.000 | 2.760 | 2.767 | See | See |
KNeighborsClassifier_kd_tree¶scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)
All estimators share the following parameters: algorithm=kd_tree.
| estimator | function | n_samples_train | n_samples | n_features | parameters_digest | dataset_digest | mean_duration_sklearn | std_duration_sklearn | n_iter | iteration_throughput | latency | algorithm | n_jobs | n_neighbors | accuracy_score_sklearn | mean_duration_sklearnex | std_duration_sklearnex | accuracy_score_sklearnex | speedup | std_speedup | diff_accuracy_scores | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | fe8267e25dadb302ed498e50fabf7ef4 | 1eb6cf1a720a225efb91df0b529b0510 | 0.876317 | 1.276624 | NaN | 0.000091 | 0.000876 | kd_tree | -1 | 1 | 0.929 | 0.119179 | 0.003178 | 0.910 | 7.352972 | 7.355586 | 0.019 |
| 4 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | dc74b969bf622bc24ba3bc62c980983b | 1eb6cf1a720a225efb91df0b529b0510 | 1.097765 | 0.502636 | NaN | 0.000073 | 0.001098 | kd_tree | -1 | 5 | 0.946 | 0.206966 | 0.009610 | 0.941 | 5.304086 | 5.309801 | 0.005 |
| 7 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | 41accfcdcd5c50784bedf6164b63de99 | 1eb6cf1a720a225efb91df0b529b0510 | 5.663641 | 0.742520 | NaN | 0.000014 | 0.005664 | kd_tree | 1 | 100 | 0.951 | 0.629654 | 0.005520 | 0.940 | 8.994844 | 8.995189 | 0.011 |
| 10 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | 022f7445d43bb1dbc24dc3106c03cb93 | 1eb6cf1a720a225efb91df0b529b0510 | 3.071856 | 0.116202 | NaN | 0.000026 | 0.003072 | kd_tree | -1 | 100 | 0.951 | 0.607333 | 0.004828 | 0.940 | 5.057945 | 5.058105 | 0.011 |
| 13 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | 84622ba45e941db642965553529e1941 | 1eb6cf1a720a225efb91df0b529b0510 | 1.694613 | 0.341814 | NaN | 0.000047 | 0.001695 | kd_tree | 1 | 5 | 0.946 | 0.212676 | 0.001831 | 0.941 | 7.968042 | 7.968337 | 0.005 |
| 16 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | a9455565db1d8e052a783317c99744ff | 1eb6cf1a720a225efb91df0b529b0510 | 0.947502 | 0.346885 | NaN | 0.000084 | 0.000948 | kd_tree | 1 | 1 | 0.929 | 0.111416 | 0.000950 | 0.910 | 8.504203 | 8.504513 | 0.019 |
| 19 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | fe8267e25dadb302ed498e50fabf7ef4 | edf7cb014c2c0910e1c3aaaf8ae633f2 | 0.027441 | 0.020019 | NaN | 0.000583 | 0.000027 | kd_tree | -1 | 1 | 0.891 | 0.000384 | 0.000043 | 0.879 | 71.388119 | 71.841598 | 0.012 |
| 22 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | dc74b969bf622bc24ba3bc62c980983b | edf7cb014c2c0910e1c3aaaf8ae633f2 | 0.022126 | 0.000991 | NaN | 0.000723 | 0.000022 | kd_tree | -1 | 5 | 0.911 | 0.000644 | 0.000021 | 0.905 | 34.373395 | 34.391083 | 0.006 |
| 25 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | 41accfcdcd5c50784bedf6164b63de99 | edf7cb014c2c0910e1c3aaaf8ae633f2 | 0.036908 | 0.014079 | NaN | 0.000434 | 0.000037 | kd_tree | 1 | 100 | 0.894 | 0.004448 | 0.000025 | 0.917 | 8.297963 | 8.298090 | 0.023 |
| 28 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | 022f7445d43bb1dbc24dc3106c03cb93 | edf7cb014c2c0910e1c3aaaf8ae633f2 | 0.040035 | 0.009418 | NaN | 0.000400 | 0.000040 | kd_tree | -1 | 100 | 0.894 | 0.005172 | 0.001425 | 0.917 | 7.740200 | 8.028531 | 0.023 |
| 31 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | 84622ba45e941db642965553529e1941 | edf7cb014c2c0910e1c3aaaf8ae633f2 | 0.019368 | 0.000162 | NaN | 0.000826 | 0.000019 | kd_tree | 1 | 5 | 0.911 | 0.000628 | 0.000025 | 0.905 | 30.859800 | 30.883629 | 0.006 |
| 34 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | a9455565db1d8e052a783317c99744ff | edf7cb014c2c0910e1c3aaaf8ae633f2 | 0.018363 | 0.000173 | NaN | 0.000871 | 0.000018 | kd_tree | 1 | 1 | 0.891 | 0.000381 | 0.000033 | 0.879 | 48.137254 | 48.315735 | 0.012 |
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | iteration_throughput | latency | n_jobs | n_neighbors | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | 3.028 | 0.029 | 0.026 | 0.0 | -1 | 1 | 0.808 | 0.084 | 3.746 | 3.767 | See | See |
| 3 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | 3.635 | 0.078 | 0.022 | 0.0 | -1 | 5 | 0.731 | 0.011 | 4.975 | 4.975 | See | See |
| 6 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | 3.588 | 0.084 | 0.022 | 0.0 | 1 | 100 | 0.776 | 0.004 | 4.621 | 4.621 | See | See |
| 9 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | 3.617 | 0.111 | 0.022 | 0.0 | -1 | 100 | 0.723 | 0.006 | 5.003 | 5.003 | See | See |
| 12 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | 3.642 | 0.060 | 0.022 | 0.0 | 1 | 5 | 0.772 | 0.003 | 4.719 | 4.719 | See | See |
| 15 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | 3.630 | 0.062 | 0.022 | 0.0 | 1 | 1 | 0.730 | 0.016 | 4.973 | 4.974 | See | See |
| 18 | KNeighborsClassifier_kd_tree | fit | 1000 | 1000 | 2 | 0.001 | 0.001 | 0.017 | 0.0 | -1 | 1 | 0.005 | 0.003 | 0.199 | 0.230 | See | See |
| 21 | KNeighborsClassifier_kd_tree | fit | 1000 | 1000 | 2 | 0.001 | 0.000 | 0.032 | 0.0 | -1 | 5 | 0.003 | 0.003 | 0.159 | 0.231 | See | See |
| 24 | KNeighborsClassifier_kd_tree | fit | 1000 | 1000 | 2 | 0.001 | 0.000 | 0.031 | 0.0 | 1 | 100 | 0.001 | 0.001 | 0.414 | 0.588 | See | See |
| 27 | KNeighborsClassifier_kd_tree | fit | 1000 | 1000 | 2 | 0.001 | 0.000 | 0.031 | 0.0 | -1 | 100 | 0.001 | 0.002 | 0.360 | 0.597 | See | See |
| 30 | KNeighborsClassifier_kd_tree | fit | 1000 | 1000 | 2 | 0.001 | 0.000 | 0.032 | 0.0 | 1 | 5 | 0.001 | 0.000 | 0.646 | 0.647 | See | See |
| 33 | KNeighborsClassifier_kd_tree | fit | 1000 | 1000 | 2 | 0.001 | 0.000 | 0.032 | 0.0 | 1 | 1 | 0.001 | 0.000 | 0.636 | 0.637 | See | See |
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | iteration_throughput | latency | n_jobs | n_neighbors | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | 0.876 | 1.277 | 0.000 | 0.001 | -1 | 1 | 0.119 | 0.003 | 7.353 | 7.356 | See | See |
| 2 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | 0.003 | 0.000 | 0.000 | 0.003 | -1 | 1 | 0.000 | 0.000 | 10.127 | 10.572 | See | See |
| 4 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | 1.098 | 0.503 | 0.000 | 0.001 | -1 | 5 | 0.207 | 0.010 | 5.304 | 5.310 | See | See |
| 5 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | 0.003 | 0.001 | 0.000 | 0.003 | -1 | 5 | 0.000 | 0.000 | 7.796 | 8.268 | See | See |
| 7 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | 5.664 | 0.743 | 0.000 | 0.006 | 1 | 100 | 0.630 | 0.006 | 8.995 | 8.995 | See | See |
| 8 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | 0.003 | 0.001 | 0.000 | 0.003 | 1 | 100 | 0.001 | 0.000 | 4.014 | 4.193 | See | See |
| 10 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | 3.072 | 0.116 | 0.000 | 0.003 | -1 | 100 | 0.607 | 0.005 | 5.058 | 5.058 | See | See |
| 11 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | 0.005 | 0.001 | 0.000 | 0.005 | -1 | 100 | 0.001 | 0.000 | 6.769 | 7.150 | See | See |
| 13 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | 1.695 | 0.342 | 0.000 | 0.002 | 1 | 5 | 0.213 | 0.002 | 7.968 | 7.968 | See | See |
| 14 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | 0.002 | 0.000 | 0.000 | 0.002 | 1 | 5 | 0.000 | 0.000 | 3.575 | 3.874 | See | See |
| 16 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | 0.948 | 0.347 | 0.000 | 0.001 | 1 | 1 | 0.111 | 0.001 | 8.504 | 8.505 | See | See |
| 17 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 1 | 0.000 | 0.000 | 3.816 | 4.076 | See | See |
| 19 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | 0.027 | 0.020 | 0.001 | 0.000 | -1 | 1 | 0.000 | 0.000 | 71.388 | 71.842 | See | See |
| 20 | KNeighborsClassifier_kd_tree | predict | 1000 | 1 | 2 | 0.002 | 0.000 | 0.000 | 0.002 | -1 | 1 | 0.000 | 0.000 | 24.379 | 25.386 | See | See |
| 22 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | 0.022 | 0.001 | 0.001 | 0.000 | -1 | 5 | 0.001 | 0.000 | 34.373 | 34.391 | See | See |
| 23 | KNeighborsClassifier_kd_tree | predict | 1000 | 1 | 2 | 0.002 | 0.000 | 0.000 | 0.002 | -1 | 5 | 0.000 | 0.000 | 23.904 | 24.798 | See | See |
| 25 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | 0.037 | 0.014 | 0.000 | 0.000 | 1 | 100 | 0.004 | 0.000 | 8.298 | 8.298 | See | See |
| 26 | KNeighborsClassifier_kd_tree | predict | 1000 | 1 | 2 | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 100 | 0.000 | 0.000 | 6.132 | 6.459 | See | See |
| 28 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | 0.040 | 0.009 | 0.000 | 0.000 | -1 | 100 | 0.005 | 0.001 | 7.740 | 8.029 | See | See |
| 29 | KNeighborsClassifier_kd_tree | predict | 1000 | 1 | 2 | 0.002 | 0.000 | 0.000 | 0.002 | -1 | 100 | 0.000 | 0.000 | 21.096 | 21.866 | See | See |
| 31 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | 0.019 | 0.000 | 0.001 | 0.000 | 1 | 5 | 0.001 | 0.000 | 30.860 | 30.884 | See | See |
| 32 | KNeighborsClassifier_kd_tree | predict | 1000 | 1 | 2 | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 5 | 0.000 | 0.000 | 6.467 | 6.872 | See | See |
| 34 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | 0.018 | 0.000 | 0.001 | 0.000 | 1 | 1 | 0.000 | 0.000 | 48.137 | 48.316 | See | See |
| 35 | KNeighborsClassifier_kd_tree | predict | 1000 | 1 | 2 | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 1 | 0.000 | 0.000 | 6.611 | 6.971 | See | See |
KMeans_tall¶scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)
All estimators share the following parameters: algorithm=full, n_clusters=3, max_iter=30, n_init=1, tol=1e-16.
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | n_iter | iteration_throughput | latency | init | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KMeans_tall | fit | 1000000 | 1000000 | 2 | 0.525 | 0.102 | 30 | 0.030 | 0.0 | random | 0.378 | 0.028 | 1.390 | 1.394 | See | See |
| 3 | KMeans_tall | fit | 1000000 | 1000000 | 2 | 0.559 | 0.020 | 30 | 0.029 | 0.0 | k-means++ | 0.406 | 0.025 | 1.379 | 1.382 | See | See |
| 6 | KMeans_tall | fit | 1000000 | 1000000 | 100 | 5.701 | 0.282 | 30 | 0.140 | 0.0 | random | 2.648 | 0.023 | 2.153 | 2.153 | See | See |
| 9 | KMeans_tall | fit | 1000000 | 1000000 | 100 | 5.739 | 0.008 | 30 | 0.139 | 0.0 | k-means++ | 2.808 | 0.013 | 2.044 | 2.044 | See | See |
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | n_iter | iteration_throughput | latency | init | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | KMeans_tall | predict | 1000000 | 1000 | 2 | 0.002 | 0.001 | 30 | 0.010 | 0.000 | random | 0.0 | 0.0 | 9.636 | 15.250 | See | See |
| 2 | KMeans_tall | predict | 1000000 | 1 | 2 | 0.002 | 0.000 | 30 | 0.000 | 0.002 | random | 0.0 | 0.0 | 7.294 | 14.260 | See | See |
| 4 | KMeans_tall | predict | 1000000 | 1000 | 2 | 0.002 | 0.001 | 30 | 0.010 | 0.000 | k-means++ | 0.0 | 0.0 | 11.743 | 13.491 | See | See |
| 5 | KMeans_tall | predict | 1000000 | 1 | 2 | 0.001 | 0.000 | 30 | 0.000 | 0.001 | k-means++ | 0.0 | 0.0 | 14.270 | 15.143 | See | See |
| 7 | KMeans_tall | predict | 1000000 | 1000 | 100 | 0.002 | 0.000 | 30 | 0.468 | 0.000 | random | 0.0 | 0.0 | 7.174 | 7.735 | See | See |
| 8 | KMeans_tall | predict | 1000000 | 1 | 100 | 0.001 | 0.000 | 30 | 0.001 | 0.001 | random | 0.0 | 0.0 | 13.085 | 13.501 | See | See |
| 10 | KMeans_tall | predict | 1000000 | 1000 | 100 | 0.002 | 0.000 | 30 | 0.485 | 0.000 | k-means++ | 0.0 | 0.0 | 6.815 | 7.381 | See | See |
| 11 | KMeans_tall | predict | 1000000 | 1 | 100 | 0.001 | 0.000 | 30 | 0.001 | 0.001 | k-means++ | 0.0 | 0.0 | 15.454 | 15.935 | See | See |
KMeans_short¶scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)
All estimators share the following parameters: algorithm=full, n_clusters=300, max_iter=20, n_init=1, tol=1e-16.
| estimator | function | n_samples_train | n_samples | n_features | parameters_digest | dataset_digest | mean_duration_sklearn | std_duration_sklearn | n_iter | iteration_throughput | latency | algorithm | init | max_iter | n_clusters | n_init | tol | adjusted_rand_score_sklearn | mean_duration_sklearnex | std_duration_sklearnex | adjusted_rand_score_sklearnex | speedup | std_speedup | diff_adjusted_rand_scores | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | KMeans_short | predict | 10000 | 1000 | 2 | 3db9c030f488d0e8ff350ae449da4627 | 058b7e3842b587a5c675518f0706f5ee | 0.001804 | 0.000176 | 20 | 0.008871 | 0.000002 | full | random | 20 | 300 | 1 | 1.000000e-16 | 0.000126 | 0.000418 | 0.000037 | -0.000965 | 4.313887 | 4.330867 | 0.001090 |
| 4 | KMeans_short | predict | 10000 | 1000 | 2 | ac9b8cec4749455c0d1cd1a92c150716 | 058b7e3842b587a5c675518f0706f5ee | 0.001684 | 0.000102 | 20 | 0.009504 | 0.000002 | full | k-means++ | 20 | 300 | 1 | 1.000000e-16 | 0.001245 | 0.000433 | 0.000044 | -0.000750 | 3.886308 | 3.906478 | 0.001995 |
| 7 | KMeans_short | predict | 10000 | 1000 | 100 | 3db9c030f488d0e8ff350ae449da4627 | 7f85b913f395ec54101a6738ea63a9a7 | 0.002548 | 0.000289 | 20 | 0.313976 | 0.000003 | full | random | 20 | 300 | 1 | 1.000000e-16 | 0.278733 | 0.000929 | 0.000072 | 0.293767 | 2.741507 | 2.749833 | 0.015034 |
| 10 | KMeans_short | predict | 10000 | 1000 | 100 | ac9b8cec4749455c0d1cd1a92c150716 | 7f85b913f395ec54101a6738ea63a9a7 | 0.002426 | 0.000195 | 20 | 0.329744 | 0.000002 | full | k-means++ | 20 | 300 | 1 | 1.000000e-16 | 0.317011 | 0.000921 | 0.000072 | 0.256968 | 2.635393 | 2.643476 | 0.060044 |
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | n_iter | iteration_throughput | latency | init | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KMeans_short | fit | 10000 | 10000 | 2 | 0.072 | 0.001 | 20 | 0.002 | 0.0 | random | 0.027 | 0.005 | 2.684 | 2.725 | See | See |
| 3 | KMeans_short | fit | 10000 | 10000 | 2 | 0.208 | 0.002 | 20 | 0.001 | 0.0 | k-means++ | 0.081 | 0.001 | 2.570 | 2.570 | See | See |
| 6 | KMeans_short | fit | 10000 | 10000 | 100 | 0.189 | 0.002 | 20 | 0.042 | 0.0 | random | 0.106 | 0.001 | 1.793 | 1.793 | See | See |
| 9 | KMeans_short | fit | 10000 | 10000 | 100 | 0.550 | 0.008 | 20 | 0.015 | 0.0 | k-means++ | 0.308 | 0.004 | 1.787 | 1.788 | See | See |
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | n_iter | iteration_throughput | latency | init | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | KMeans_short | predict | 10000 | 1000 | 2 | 0.002 | 0.0 | 20 | 0.009 | 0.000 | random | 0.000 | 0.0 | 4.314 | 4.331 | See | See |
| 2 | KMeans_short | predict | 10000 | 1 | 2 | 0.001 | 0.0 | 20 | 0.000 | 0.001 | random | 0.000 | 0.0 | 13.461 | 13.866 | See | See |
| 4 | KMeans_short | predict | 10000 | 1000 | 2 | 0.002 | 0.0 | 20 | 0.010 | 0.000 | k-means++ | 0.000 | 0.0 | 3.886 | 3.906 | See | See |
| 5 | KMeans_short | predict | 10000 | 1 | 2 | 0.001 | 0.0 | 20 | 0.000 | 0.001 | k-means++ | 0.000 | 0.0 | 11.537 | 11.861 | See | See |
| 7 | KMeans_short | predict | 10000 | 1000 | 100 | 0.003 | 0.0 | 20 | 0.314 | 0.000 | random | 0.001 | 0.0 | 2.742 | 2.750 | See | See |
| 8 | KMeans_short | predict | 10000 | 1 | 100 | 0.001 | 0.0 | 20 | 0.001 | 0.001 | random | 0.000 | 0.0 | 10.410 | 10.552 | See | See |
| 10 | KMeans_short | predict | 10000 | 1000 | 100 | 0.002 | 0.0 | 20 | 0.330 | 0.000 | k-means++ | 0.001 | 0.0 | 2.635 | 2.643 | See | See |
| 11 | KMeans_short | predict | 10000 | 1 | 100 | 0.001 | 0.0 | 20 | 0.001 | 0.001 | k-means++ | 0.000 | 0.0 | 9.582 | 9.769 | See | See |
LogisticRegression¶scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)
All estimators share the following parameters: penalty=l2, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=nan, random_state=nan, solver=lbfgs, max_iter=100, multi_class=auto, verbose=0, warm_start=False, n_jobs=nan, l1_ratio=nan.
| estimator | function | n_samples_train | n_samples | n_features | parameters_digest | dataset_digest | mean_duration_sklearn | std_duration_sklearn | n_iter | iteration_throughput | latency | C | class_weight | dual | fit_intercept | intercept_scaling | l1_ratio | max_iter | multi_class | n_jobs | penalty | random_state | solver | tol | verbose | warm_start | accuracy_score_sklearn | mean_duration_sklearnex | std_duration_sklearnex | accuracy_score_sklearnex | speedup | std_speedup | diff_accuracy_scores | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | LogisticRegression | predict | 1000000 | 1000 | 100 | f11be2529b0a77bc8fb5ee8de141e6b2 | 09c36ed6521e03974d0c134895f56c01 | 0.000604 | 0.001094 | [20] | 1.323917 | 6.042677e-07 | 1.0 | NaN | False | True | 1 | NaN | 100 | auto | NaN | l2 | NaN | lbfgs | 0.0001 | 0 | False | 0.56 | 0.000599 | 0.000952 | 0.55 | 1.008511 | 1.893963 | 0.01 |
| 4 | LogisticRegression | predict | 1000 | 100 | 10000 | f11be2529b0a77bc8fb5ee8de141e6b2 | 7daecaea01e5baa8c862f264e3c1c3b0 | 0.001518 | 0.000292 | [26] | 5.270023 | 1.518020e-05 | 1.0 | NaN | False | True | 1 | NaN | 100 | auto | NaN | l2 | NaN | lbfgs | 0.0001 | 0 | False | 0.35 | 0.003034 | 0.000221 | 0.28 | 0.500362 | 0.501686 | 0.07 |
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | n_iter | iteration_throughput | latency | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | LogisticRegression | fit | 1000000 | 1000000 | 100 | 11.142 | 0.497 | [20] | 0.072 | 0.000 | 1.974 | 0.093 | 5.645 | 5.651 | See | See |
| 3 | LogisticRegression | fit | 1000 | 1000 | 10000 | 1.106 | 1.066 | [26] | 0.072 | 0.001 | 0.834 | 0.020 | 1.325 | 1.326 | See | See |
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | n_iter | iteration_throughput | latency | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | LogisticRegression | predict | 1000000 | 1000 | 100 | 0.001 | 0.001 | [20] | 1.324 | 0.0 | 0.001 | 0.001 | 1.009 | 1.894 | See | See |
| 2 | LogisticRegression | predict | 1000000 | 1 | 100 | 0.000 | 0.000 | [20] | 0.015 | 0.0 | 0.000 | 0.000 | 0.379 | 0.384 | See | See |
| 4 | LogisticRegression | predict | 1000 | 100 | 10000 | 0.002 | 0.000 | [26] | 5.270 | 0.0 | 0.003 | 0.000 | 0.500 | 0.502 | See | See |
| 5 | LogisticRegression | predict | 1000 | 1 | 10000 | 0.000 | 0.000 | [26] | 1.096 | 0.0 | 0.001 | 0.000 | 0.121 | 0.121 | See | See |
Ridge¶scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)
All estimators share the following parameters: alpha=1.0, fit_intercept=True, normalize=deprecated, copy_X=True, max_iter=nan, tol=0.001, solver=auto, random_state=nan.
| estimator | function | n_samples_train | n_samples | n_features | parameters_digest | dataset_digest | mean_duration_sklearn | std_duration_sklearn | n_iter | iteration_throughput | latency | alpha | copy_X | fit_intercept | max_iter | normalize | random_state | solver | tol | r2_score_sklearn | mean_duration_sklearnex | std_duration_sklearnex | r2_score_sklearnex | speedup | std_speedup | diff_r2_scores | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | Ridge | predict | 1000 | 1000 | 10000 | a70ae8dc5c059ec88cf27c61016aee2a | 14fae294d530397144c65ba3209bf125 | 0.011523 | 0.000269 | NaN | 6.942915 | 0.000012 | 1.0 | True | True | NaN | deprecated | NaN | auto | 0.001 | 0.082567 | 0.018706 | 0.000485 | 0.122191 | 0.615986 | 0.616193 | 0.039624 |
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | iteration_throughput | latency | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Ridge | fit | 1000 | 1000 | 10000 | 0.173 | 0.003 | 0.463 | 0.0 | 0.179 | 0.001 | 0.968 | 0.968 | See | See |
| 3 | Ridge | fit | 1000000 | 1000000 | 100 | 1.127 | 0.097 | 0.710 | 0.0 | 0.304 | 0.240 | 3.703 | 4.715 | See | See |
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | iteration_throughput | latency | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | Ridge | predict | 1000 | 1000 | 10000 | 0.012 | 0.0 | 6.943 | 0.0 | 0.019 | 0.0 | 0.616 | 0.616 | See | See |
| 2 | Ridge | predict | 1000 | 1 | 10000 | 0.000 | 0.0 | 1.416 | 0.0 | 0.000 | 0.0 | 0.592 | 0.636 | See | See |
| 4 | Ridge | predict | 1000000 | 1000 | 100 | 0.000 | 0.0 | 5.669 | 0.0 | 0.000 | 0.0 | 0.423 | 0.738 | See | See |
| 5 | Ridge | predict | 1000000 | 1 | 100 | 0.000 | 0.0 | 0.017 | 0.0 | 0.000 | 0.0 | 0.601 | 0.636 | See | See |